Abstract—Smart-agriculture technologies comprise a set of management systems designed to sustainably increase the efficiency and productivity of farming. In this paper, we present a lab-on-a-chip device that can be employed as a plant disease forecasting tool for canola crop. Our device can be employed as a platform to forecast potential outbreaks of one of the most devastating diseases of canola and other crops, Sclerotinia stem rot. The system consists of a microfluidic chip capable of detecting single airborne Sclerotinia sclerotiorum ascospores. Target ascospores are injected in the chip and selectively captured by dielectrophoresis, while others spores in the sample are flushed away. Afterward, captured ascospores are released into the flow stream of the channel and are detected employing electrochemical impedance spectroscopy and coplanar microelectrodes. Our device provides a design for a low-cost, miniaturized, and automated platform technology for airborne spore detection and disease prevention. Index Terms— Smart-agriculture, Microfluidic, Impedance, Canola, and Sclerotinia stem rot. I. INTRODUCTION CLEROTINIA sclerotiorum has long been identified as a fungal pathogen for more than 400 plant species around the globe, including canola, one of the most widely grown oilseeds [1]. Sclerotinia stem rot (SSR) of canola is a devastating disease responsible for significant yield losses that can be as high as 50% under certain environmental conditions [2]. Canola is a profitable crop with a huge impact on the economy of countries like Canada, China, and India, the biggest canola producing countries in the world. In Canada alone, the production of canola generates $26.7 billion annually [3], with an increase in production from about 12.7 million tons in 2010 to 20.4 million tons in 2018 [4]. Currently, the major threat to canola production is SSR. This work was supported by Canola Council of Canada, Agri-Science Research Cluster, Alberta Innovates and InnoTech Alberta Inc., and MITACS Canada. P. A. Duarte, L. Menze, G. N. Abdelrasoul, R. Stuermer and J. Chen are with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada (e-mail: jc65@ ualberta.ca) S. Yosinski, Z. Kobos and M. Reed are with the School of Engineering and Applied Science, Yale University, New Haven, CT 06511, USA (email: mark.reed@yale.edu ) J. Yang and X. S. Li are with InnoTech Alberta Inc., Edmonton, AB T6B 3T9, Canada (e- mail: susie.Li@innotechalberta.ca) S. sclerotiorum can survive in the soil for years as small structures called sclerotia, which germinate when conditions are suitable, such as saturated soil and cool environments [5]. The germination of sclerotia gives rise to mushroom-like structures called apothecia, which can produce millions of microscopic airborne spores (ascospores) with a typical size range of 9–14 μm that can travel from field to field following air currents. Canola infection occurs mainly due to these ascospores, which land on the plant petals and germinate once they fall onto leaves, stems, and branches, producing hyphae, which penetrate the plant. The infected plant then will produce sclerotia, completing the disease cycle [5]. SSR management is limited to two primary methods: crop rotation with non-host species and fungicide application. Because sclerotia can survive in the soil for up to 7 years [5], crop rotation is not effective enough to mitigate SSR. Fungicides, on the other hand, are expensive and need to be applied during specific stages of infection, which is difficult as SSR outbreaks are hard to predict [6]. Due to these limitations, there has been great interest in developing SSR forecasting systems that can provide farmers with reliable information, which enables them to make objective spray decisions. Forecasting systems based on weather-maps have already been employed [7], [8]. However, these systems are limited because they only indicate the regional risk of an outbreak, ignoring characteristics of specific-field microclimates. Forecasting models that Single Ascospore Detection for the Forecasting of Sclerotinia Stem Rot of Canola Pedro A. Duarte, Lukas Menze, Gaser N. Abdelrasoul, Shari Yosinski, Zak Kobos, Riley Stuermer, Mark Reed, Jian Yang, Xiujie S. Li and Jie Chen S Page 1 of 8 Lab on a Chip Lab on a Chip Accepted Manuscript Published on 31 August 2020. Downloaded by Yale University Library on 9/2/2020 2:41:43 PM. View Article Online DOI: 10.1039/D0LC00426J